论文标题
通过社会意识推荐改善智能会议的参与
Improving Smart Conference Participation through Socially-Aware Recommendation
论文作者
论文摘要
这项研究介绍了在智能会议上推荐给参与者的演讲。我们提出了一种场地建议算法，对场地和环境的社会意识建议（SARVE）。 Sarve计算会议参与者的相关性和社会特征信息。为了使用分布式社区检测来建模推荐过程，Sarve进一步整合了智能会议社区和参与者的当前环境。萨尔夫（Sarve）建议对每个参与者感兴趣的演讲会议。我们使用现实世界数据集评估SARVE。在我们的实验中，我们将SARVE与两种相关的最先进方法进行了比较，即：上下文感知的移动推荐服务（CAMR）和会议导航器（推荐人）模型。我们的实验结果表明，就使用的评估指标而言：精确，召回和F量，Sarve取得了更可靠和有利的社会（关系和背景）建议结果。
This research addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm, Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real world dataset. In our experiments, we compare SARVE to two related state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services (CAMRS) and Conference Navigator (Recommender) Model. Our experimental results show that in terms of the utilized evaluation metrics: precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.
